1. Field of the Invention
The present invention relates to a method and system for face detection, and more particularly, to a method and system for detecting a face using a pattern classifier, which previously learned from the face images and near-face images.
2. Description of the Related Art
Face detection is not only used as a first step required in face recognition systems, but also used in various applications such as intelligent human-computer interfaces, video monitoring systems, and content-based image search and retrieval by using a face. Although research on face detection has recently increased a lot, the reliability and speed of face detection algorithms is so low that the technology is practically unused.
To solve these problems, methods using a decision boundary learned from a face sample pattern in detecting a face have been investigated. Representative methods are methods using multi layer perception (MLP) and methods using support vector machine (SVM).
In one conventional approach for MLP, a local receptive field is applied to a face image. In the conventional approach for MLP, an image pattern is projected onto a plurality of principle component analysis (PCA) subspaces, and distances from the image pattern to the individual subspaces are used as inputs for MLP.
However, since a learning method using MLP just minimizes errors from given sample data, operations are satisfactorily performed with respect to the learned data, but successful operations cannot be secured with respect to new data that has not been learned. In particular, when considering various changes in a face image due to factors such as light, facial expression, and pose, the reliability of the methods based on MLP decreases if a large number of samples are not used in the training stage.
Conversely, an SVM minimizes errors in given data and maximizes the margin of the entire system, so it is more applicable to a new pattern as compared to the MLP. Although a conventional technique that directly applies an SVM to a face image results in a reliable face detection up to some degree, the technique is not satisfactory yet to be applied in real life. Another conventional technique that extracts the features of a face using independent component analysis (ICA) and applies an SVM to the face features has improved the reliability in detecting the face. However, since conventional techniques usually use a non-linear SVM in order to achieve a reliable face detection performance, a large amount of calculation causes the algorithms to perform slowly.